Building a Production RAG Pipeline: From Documents to Answers

A technical guide to chunking, retrieval, re-ranking, and observability for RAG systems at scale A retrieval-augmented generation (RAG) pipeline is a system that fetches relevant documents from a knowledge base and passes them to a language model as context, enabling the model to answer questions grounded in factual, up-to-date information. The architecture sounds simple: ingest documents, chunk them, embed them, index them, retrieve relevant chunks at query time, optionally re-rank, then pass…